Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations2617
Missing cells4983
Missing cells (%)7.9%
Duplicate rows49
Duplicate rows (%)1.9%
Total size in memory490.8 KiB
Average record size in memory192.1 B

Variable types

Numeric12
Categorical4
DateTime5
Text3

Alerts

Dataset has 49 (1.9%) duplicate rowsDuplicates
area is highly overall correlated with pond_length and 2 other fieldsHigh correlation
id is highly overall correlated with pond_idHigh correlation
pond_depth is highly overall correlated with species_idHigh correlation
pond_id is highly overall correlated with id and 1 other fieldsHigh correlation
pond_length is highly overall correlated with area and 2 other fieldsHigh correlation
pond_width is highly overall correlated with area and 3 other fieldsHigh correlation
species_id is highly overall correlated with pond_depth and 4 other fieldsHigh correlation
target_cultivation_day is highly overall correlated with species_idHigh correlation
total_seed is highly overall correlated with area and 1 other fieldsHigh correlation
extracted_at is highly imbalanced (97.0%)Imbalance
subscription_type is highly imbalanced (61.5%)Imbalance
species_id has 814 (31.1%) missing valuesMissing
remark has 1281 (48.9%) missing valuesMissing
initial_age has 48 (1.8%) missing valuesMissing
ordered_at has 1523 (58.2%) missing valuesMissing
hatchery_id has 465 (17.8%) missing valuesMissing
total_seed_type has 242 (9.2%) missing valuesMissing
hatchery_name has 465 (17.8%) missing valuesMissing
pond_depth has 118 (4.5%) missing valuesMissing
area is highly skewed (γ1 = 25.54805684)Skewed
limit_weight_per_area is highly skewed (γ1 = 33.62644159)Skewed
target_size is highly skewed (γ1 = 28.2975653)Skewed
pond_depth is highly skewed (γ1 = 49.59306466)Skewed
initial_age has 2199 (84.0%) zerosZeros

Reproduction

Analysis started2024-08-14 09:27:37.939511
Analysis finished2024-08-14 09:28:12.240290
Duration34.3 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION 

Distinct2500
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19900.667
Minimum3458
Maximum29874
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2024-08-14T16:28:12.510667image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum3458
5-th percentile8941.2
Q116091
median20401
Q324804
95-th percentile27960.8
Maximum29874
Range26416
Interquartile range (IQR)8713

Descriptive statistics

Standard deviation6006.8024
Coefficient of variation (CV)0.30183925
Kurtosis-0.41509212
Mean19900.667
Median Absolute Deviation (MAD)4353
Skewness-0.53693061
Sum52080046
Variance36081675
MonotonicityNot monotonic
2024-08-14T16:28:12.946566image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20124 4
 
0.2%
20189 4
 
0.2%
20190 4
 
0.2%
24519 3
 
0.1%
24304 3
 
0.1%
23328 3
 
0.1%
23945 3
 
0.1%
24308 3
 
0.1%
23327 3
 
0.1%
24522 3
 
0.1%
Other values (2490) 2584
98.7%
ValueCountFrequency (%)
3458 1
< 0.1%
3459 1
< 0.1%
4036 1
< 0.1%
4038 1
< 0.1%
4039 1
< 0.1%
4044 1
< 0.1%
4045 1
< 0.1%
4046 1
< 0.1%
4047 1
< 0.1%
4048 1
< 0.1%
ValueCountFrequency (%)
29874 1
< 0.1%
29873 1
< 0.1%
29679 1
< 0.1%
29659 1
< 0.1%
29619 1
< 0.1%
29597 1
< 0.1%
29579 1
< 0.1%
29557 1
< 0.1%
29518 1
< 0.1%
29513 1
< 0.1%

pond_id
Real number (ℝ)

HIGH CORRELATION 

Distinct1675
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32218.569
Minimum1
Maximum47282
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2024-08-14T16:28:13.329183image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12549.8
Q124570
median34697
Q338943
95-th percentile45364.2
Maximum47282
Range47281
Interquartile range (IQR)14373

Descriptive statistics

Standard deviation10156.254
Coefficient of variation (CV)0.31522984
Kurtosis0.28399642
Mean32218.569
Median Absolute Deviation (MAD)5224
Skewness-0.91142781
Sum84315995
Variance1.031495 × 108
MonotonicityNot monotonic
2024-08-14T16:28:13.731868image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24265 8
 
0.3%
34746 8
 
0.3%
24569 8
 
0.3%
34748 7
 
0.3%
30344 7
 
0.3%
38474 6
 
0.2%
37209 6
 
0.2%
21226 6
 
0.2%
37102 6
 
0.2%
33945 6
 
0.2%
Other values (1665) 2549
97.4%
ValueCountFrequency (%)
1 5
0.2%
2 3
0.1%
3 3
0.1%
4 4
0.2%
5 3
0.1%
6 3
0.1%
7 3
0.1%
8 3
0.1%
11 1
 
< 0.1%
6738 3
0.1%
ValueCountFrequency (%)
47282 1
< 0.1%
47109 1
< 0.1%
47108 1
< 0.1%
47107 1
< 0.1%
47103 1
< 0.1%
47090 1
< 0.1%
47061 1
< 0.1%
47000 1
< 0.1%
46978 1
< 0.1%
46910 1
< 0.1%

species_id
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.1%
Missing814
Missing (%)31.1%
Memory size20.6 KiB
1.0
1598 
2.0
205 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5409
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1598
61.1%
2.0 205
 
7.8%
(Missing) 814
31.1%

Length

2024-08-14T16:28:14.129973image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-14T16:28:14.442501image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1598
88.6%
2.0 205
 
11.4%

Most occurring characters

ValueCountFrequency (%)
. 1803
33.3%
0 1803
33.3%
1 1598
29.5%
2 205
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5409
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 1803
33.3%
0 1803
33.3%
1 1598
29.5%
2 205
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5409
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 1803
33.3%
0 1803
33.3%
1 1598
29.5%
2 205
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5409
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 1803
33.3%
0 1803
33.3%
1 1598
29.5%
2 205
 
3.8%

total_seed
Real number (ℝ)

HIGH CORRELATION 

Distinct868
Distinct (%)33.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219755.17
Minimum10
Maximum1800000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2024-08-14T16:28:14.775857image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile25500
Q196000
median194392
Q3300000
95-th percentile500000
Maximum1800000
Range1799990
Interquartile range (IQR)204000

Descriptive statistics

Standard deviation171097.99
Coefficient of variation (CV)0.77858459
Kurtosis9.1539471
Mean219755.17
Median Absolute Deviation (MAD)105608
Skewness2.008268
Sum5.7509929 × 108
Variance2.9274523 × 1010
MonotonicityNot monotonic
2024-08-14T16:28:15.155285image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 126
 
4.8%
50000 112
 
4.3%
200000 86
 
3.3%
300000 84
 
3.2%
150000 79
 
3.0%
275000 60
 
2.3%
325000 54
 
2.1%
250000 54
 
2.1%
75000 51
 
1.9%
40000 40
 
1.5%
Other values (858) 1871
71.5%
ValueCountFrequency (%)
10 1
 
< 0.1%
11 1
 
< 0.1%
60 1
 
< 0.1%
100 3
0.1%
237 1
 
< 0.1%
300 1
 
< 0.1%
1272 2
0.1%
1908 2
0.1%
2543 1
 
< 0.1%
2800 2
0.1%
ValueCountFrequency (%)
1800000 1
 
< 0.1%
1600000 1
 
< 0.1%
1400000 1
 
< 0.1%
1377414 1
 
< 0.1%
1300000 1
 
< 0.1%
1200000 4
0.2%
1145064 1
 
< 0.1%
1080000 2
0.1%
1037458 1
 
< 0.1%
1000000 1
 
< 0.1%
Distinct750
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Memory size20.6 KiB
Minimum2020-03-07 00:00:00
Maximum2024-02-18 00:00:00
2024-08-14T16:28:15.494654image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:15.830252image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct857
Distinct (%)32.8%
Missing1
Missing (%)< 0.1%
Memory size20.6 KiB
Minimum2020-05-20 00:00:00
Maximum2024-04-02 00:00:00
2024-08-14T16:28:16.065368image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:16.327653image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

remark
Text

MISSING 

Distinct67
Distinct (%)5.0%
Missing1281
Missing (%)48.9%
Memory size20.6 KiB
2024-08-14T16:28:16.621747image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length117
Median length63
Mean length11.355539
Min length3

Characters and Unicode

Total characters15171
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)1.9%

Sample

1st row#TBR01
2nd row#SiklusFarm
3rd row#SiklusCustomerPermadi
4th row#SiklusTebar
5th row#SiklusFarm
ValueCountFrequency (%)
siklusfarm 276
19.7%
tbr01 185
 
13.2%
siklustebar 183
 
13.0%
vietproject23 87
 
6.2%
jt12 37
 
2.6%
sikluscustomerrizky 32
 
2.3%
tbrjember2024 31
 
2.2%
siklusbdrisda 30
 
2.1%
siklusbdrijal 27
 
1.9%
bd-katalis02 24
 
1.7%
Other values (100) 492
35.0%
2024-08-14T16:28:17.143332image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
# 1326
 
8.7%
i 1112
 
7.3%
a 1026
 
6.8%
s 939
 
6.2%
l 859
 
5.7%
u 784
 
5.2%
r 715
 
4.7%
k 712
 
4.7%
S 689
 
4.5%
B 611
 
4.0%
Other values (52) 6398
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15171
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
# 1326
 
8.7%
i 1112
 
7.3%
a 1026
 
6.8%
s 939
 
6.2%
l 859
 
5.7%
u 784
 
5.2%
r 715
 
4.7%
k 712
 
4.7%
S 689
 
4.5%
B 611
 
4.0%
Other values (52) 6398
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15171
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
# 1326
 
8.7%
i 1112
 
7.3%
a 1026
 
6.8%
s 939
 
6.2%
l 859
 
5.7%
u 784
 
5.2%
r 715
 
4.7%
k 712
 
4.7%
S 689
 
4.5%
B 611
 
4.0%
Other values (52) 6398
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15171
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
# 1326
 
8.7%
i 1112
 
7.3%
a 1026
 
6.8%
s 939
 
6.2%
l 859
 
5.7%
u 784
 
5.2%
r 715
 
4.7%
k 712
 
4.7%
S 689
 
4.5%
B 611
 
4.0%
Other values (52) 6398
42.2%
Distinct2325
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Memory size20.6 KiB
Minimum2020-02-19 08:44:53
Maximum2024-03-05 13:10:13
2024-08-14T16:28:17.376039image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:17.627596image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2289
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Memory size20.6 KiB
Minimum2020-06-03 02:44:52
Maximum2024-04-09 00:06:26
2024-08-14T16:28:17.869685image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:18.116214image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

area
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct648
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2292.6737
Minimum1.02
Maximum422500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2024-08-14T16:28:18.358630image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum1.02
5-th percentile118.62
Q1810
median1400
Q32358
95-th percentile4999.9
Maximum422500
Range422498.98
Interquartile range (IQR)1548

Descriptive statistics

Standard deviation12104.748
Coefficient of variation (CV)5.2797521
Kurtosis732.1952
Mean2292.6737
Median Absolute Deviation (MAD)682
Skewness25.548057
Sum5999927
Variance1.4652494 × 108
MonotonicityNot monotonic
2024-08-14T16:28:18.590361image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2500 155
 
5.9%
4999.9 155
 
5.9%
1000 146
 
5.6%
1600 108
 
4.1%
2000 82
 
3.1%
1200 66
 
2.5%
1500 51
 
1.9%
900 50
 
1.9%
35 49
 
1.9%
600 38
 
1.5%
Other values (638) 1717
65.6%
ValueCountFrequency (%)
1.02 2
 
0.1%
2.23 1
 
< 0.1%
6.9 1
 
< 0.1%
7 12
0.5%
7.07 9
0.3%
10 1
 
< 0.1%
19.62 9
0.3%
19.63 4
 
0.2%
20 2
 
0.1%
28.27 1
 
< 0.1%
ValueCountFrequency (%)
422500 1
< 0.1%
249998 1
< 0.1%
240000 1
< 0.1%
200000 2
0.1%
40000 2
0.1%
32000 1
< 0.1%
30000 1
< 0.1%
16000 1
< 0.1%
15000 2
0.1%
13000 1
< 0.1%

initial_age
Real number (ℝ)

MISSING  ZEROS 

Distinct38
Distinct (%)1.5%
Missing48
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean0.70494356
Minimum0
Maximum75
Zeros2199
Zeros (%)84.0%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2024-08-14T16:28:18.804492image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum75
Range75
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.7448261
Coefficient of variation (CV)6.7307887
Kurtosis128.08993
Mean0.70494356
Median Absolute Deviation (MAD)0
Skewness10.586843
Sum1811
Variance22.513375
MonotonicityNot monotonic
2024-08-14T16:28:19.029671image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 2199
84.0%
1 301
 
11.5%
10 9
 
0.3%
5 4
 
0.2%
12 4
 
0.2%
3 4
 
0.2%
6 3
 
0.1%
8 3
 
0.1%
15 3
 
0.1%
20 3
 
0.1%
Other values (28) 36
 
1.4%
(Missing) 48
 
1.8%
ValueCountFrequency (%)
0 2199
84.0%
1 301
 
11.5%
2 3
 
0.1%
3 4
 
0.2%
4 2
 
0.1%
5 4
 
0.2%
6 3
 
0.1%
7 1
 
< 0.1%
8 3
 
0.1%
10 9
 
0.3%
ValueCountFrequency (%)
75 2
0.1%
69 1
< 0.1%
66 1
< 0.1%
64 1
< 0.1%
61 1
< 0.1%
58 1
< 0.1%
49 1
< 0.1%
45 1
< 0.1%
42 1
< 0.1%
40 1
< 0.1%

limit_weight_per_area
Real number (ℝ)

SKEWED 

Distinct25
Distinct (%)1.0%
Missing7
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1.5594521
Minimum0.6
Maximum270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2024-08-14T16:28:19.700717image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.25
Q11.25
median1.25
Q31.25
95-th percentile2
Maximum270
Range269.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.6408457
Coefficient of variation (CV)4.258448
Kurtosis1213.0052
Mean1.5594521
Median Absolute Deviation (MAD)0
Skewness33.626442
Sum4070.17
Variance44.100831
MonotonicityNot monotonic
2024-08-14T16:28:20.025666image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1.25 2336
89.3%
1.2 66
 
2.5%
2.5 34
 
1.3%
2 29
 
1.1%
2.4 29
 
1.1%
1 21
 
0.8%
3 18
 
0.7%
1.5 18
 
0.7%
3.5 14
 
0.5%
4 13
 
0.5%
Other values (15) 32
 
1.2%
(Missing) 7
 
0.3%
ValueCountFrequency (%)
0.6 1
 
< 0.1%
1 21
 
0.8%
1.1 1
 
< 0.1%
1.2 66
 
2.5%
1.23 1
 
< 0.1%
1.25 2336
89.3%
1.3 5
 
0.2%
1.5 18
 
0.7%
1.6 2
 
0.1%
2 29
 
1.1%
ValueCountFrequency (%)
270 1
 
< 0.1%
166 1
 
< 0.1%
125 1
 
< 0.1%
13.9 1
 
< 0.1%
4.77 2
 
0.1%
4.7 1
 
< 0.1%
4.5 5
 
0.2%
4.25 3
 
0.1%
4 13
0.5%
3.5 14
0.5%

target_cultivation_day
Real number (ℝ)

HIGH CORRELATION 

Distinct121
Distinct (%)4.6%
Missing3
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean112.59181
Minimum0
Maximum348
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2024-08-14T16:28:20.279105image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile90
Q1100
median120
Q3120
95-th percentile148
Maximum348
Range348
Interquartile range (IQR)20

Descriptive statistics

Standard deviation23.564663
Coefficient of variation (CV)0.20929286
Kurtosis10.29678
Mean112.59181
Median Absolute Deviation (MAD)10
Skewness-0.76177839
Sum294315
Variance555.29333
MonotonicityNot monotonic
2024-08-14T16:28:20.520018image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 1193
45.6%
100 664
25.4%
90 79
 
3.0%
140 56
 
2.1%
110 51
 
1.9%
95 48
 
1.8%
130 47
 
1.8%
105 39
 
1.5%
150 34
 
1.3%
160 17
 
0.6%
Other values (111) 386
 
14.7%
ValueCountFrequency (%)
0 3
0.1%
5 4
0.2%
6 2
 
0.1%
7 3
0.1%
8 4
0.2%
9 4
0.2%
10 1
 
< 0.1%
11 5
0.2%
12 4
0.2%
13 6
0.2%
ValueCountFrequency (%)
348 1
 
< 0.1%
238 1
 
< 0.1%
220 1
 
< 0.1%
200 2
 
0.1%
194 2
 
0.1%
189 3
 
0.1%
185 8
0.3%
179 2
 
0.1%
178 3
 
0.1%
175 2
 
0.1%

target_size
Real number (ℝ)

SKEWED 

Distinct31
Distinct (%)1.2%
Missing4
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean52.696135
Minimum0
Maximum1000
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2024-08-14T16:28:20.751748image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q145
median55
Q355
95-th percentile60
Maximum1000
Range1000
Interquartile range (IQR)10

Descriptive statistics

Standard deviation22.803397
Coefficient of variation (CV)0.43273377
Kurtosis1144.5869
Mean52.696135
Median Absolute Deviation (MAD)0
Skewness28.297565
Sum137695
Variance519.99492
MonotonicityNot monotonic
2024-08-14T16:28:20.965454image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
55 1578
60.3%
45 507
 
19.4%
40 143
 
5.5%
50 87
 
3.3%
30 73
 
2.8%
60 39
 
1.5%
70 28
 
1.1%
100 27
 
1.0%
20 22
 
0.8%
35 18
 
0.7%
Other values (21) 91
 
3.5%
ValueCountFrequency (%)
0 2
 
0.1%
20 22
 
0.8%
25 11
 
0.4%
28 1
 
< 0.1%
30 73
2.8%
32 8
 
0.3%
33 2
 
0.1%
35 18
 
0.7%
36 1
 
< 0.1%
37 1
 
< 0.1%
ValueCountFrequency (%)
1000 1
 
< 0.1%
200 5
 
0.2%
150 6
 
0.2%
120 10
 
0.4%
110 1
 
< 0.1%
108 1
 
< 0.1%
100 27
1.0%
90 3
 
0.1%
86 1
 
< 0.1%
85 1
 
< 0.1%

extracted_at
Categorical

IMBALANCE 

Distinct18
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size20.6 KiB
2024-04-12 17:02:22.000
2589 
2023-05-18 17:02:11.000
 
5
2023-09-05 17:02:11.000
 
3
2023-04-21 17:02:11.000
 
3
2023-12-17 17:02:21.000
 
2
Other values (13)
 
15

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters60191
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.4%

Sample

1st row2024-04-12 17:02:22.000
2nd row2024-04-12 17:02:22.000
3rd row2024-04-12 17:02:22.000
4th row2024-04-12 17:02:22.000
5th row2024-04-12 17:02:22.000

Common Values

ValueCountFrequency (%)
2024-04-12 17:02:22.000 2589
98.9%
2023-05-18 17:02:11.000 5
 
0.2%
2023-09-05 17:02:11.000 3
 
0.1%
2023-04-21 17:02:11.000 3
 
0.1%
2023-12-17 17:02:21.000 2
 
0.1%
2023-04-09 17:02:10.000 2
 
0.1%
2022-12-28 17:02:11.000 2
 
0.1%
2023-10-22 17:08:10.000 1
 
< 0.1%
2023-04-20 17:02:11.000 1
 
< 0.1%
2023-12-21 17:02:21.000 1
 
< 0.1%
Other values (8) 8
 
0.3%

Length

2024-08-14T16:28:21.161676image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2024-04-12 2589
49.5%
17:02:22.000 2589
49.5%
17:02:11.000 20
 
0.4%
2023-05-18 5
 
0.1%
17:02:10.000 4
 
0.1%
2023-09-05 3
 
0.1%
2023-04-21 3
 
0.1%
17:02:21.000 3
 
0.1%
2023-12-17 2
 
< 0.1%
2023-04-09 2
 
< 0.1%
Other values (13) 14
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 15711
26.1%
2 15642
26.0%
1 5276
 
8.8%
- 5234
 
8.7%
: 5234
 
8.7%
4 5185
 
8.6%
7 2622
 
4.4%
2617
 
4.3%
. 2617
 
4.3%
3 25
 
< 0.1%
Other values (4) 28
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60191
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15711
26.1%
2 15642
26.0%
1 5276
 
8.8%
- 5234
 
8.7%
: 5234
 
8.7%
4 5185
 
8.6%
7 2622
 
4.4%
2617
 
4.3%
. 2617
 
4.3%
3 25
 
< 0.1%
Other values (4) 28
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60191
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15711
26.1%
2 15642
26.0%
1 5276
 
8.8%
- 5234
 
8.7%
: 5234
 
8.7%
4 5185
 
8.6%
7 2622
 
4.4%
2617
 
4.3%
. 2617
 
4.3%
3 25
 
< 0.1%
Other values (4) 28
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60191
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15711
26.1%
2 15642
26.0%
1 5276
 
8.8%
- 5234
 
8.7%
: 5234
 
8.7%
4 5185
 
8.6%
7 2622
 
4.4%
2617
 
4.3%
. 2617
 
4.3%
3 25
 
< 0.1%
Other values (4) 28
 
< 0.1%

subscription_type
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.6 KiB
Free
2420 
Paid
 
197

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters10468
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFree
2nd rowFree
3rd rowFree
4th rowFree
5th rowFree

Common Values

ValueCountFrequency (%)
Free 2420
92.5%
Paid 197
 
7.5%

Length

2024-08-14T16:28:21.358480image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-14T16:28:21.553442image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
free 2420
92.5%
paid 197
 
7.5%

Most occurring characters

ValueCountFrequency (%)
e 4840
46.2%
F 2420
23.1%
r 2420
23.1%
P 197
 
1.9%
a 197
 
1.9%
i 197
 
1.9%
d 197
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10468
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4840
46.2%
F 2420
23.1%
r 2420
23.1%
P 197
 
1.9%
a 197
 
1.9%
i 197
 
1.9%
d 197
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10468
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4840
46.2%
F 2420
23.1%
r 2420
23.1%
P 197
 
1.9%
a 197
 
1.9%
i 197
 
1.9%
d 197
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10468
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4840
46.2%
F 2420
23.1%
r 2420
23.1%
P 197
 
1.9%
a 197
 
1.9%
i 197
 
1.9%
d 197
 
1.9%

ordered_at
Date

MISSING 

Distinct865
Distinct (%)79.1%
Missing1523
Missing (%)58.2%
Memory size20.6 KiB
Minimum2020-02-19 08:44:53
Maximum2024-03-05 13:10:13
2024-08-14T16:28:21.762053image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:22.010705image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

hatchery_id
Real number (ℝ)

MISSING 

Distinct80
Distinct (%)3.7%
Missing465
Missing (%)17.8%
Infinite0
Infinite (%)0.0%
Mean412.30437
Minimum1
Maximum1077
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2024-08-14T16:28:22.265790image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q135
median83
Q31004
95-th percentile1049
Maximum1077
Range1076
Interquartile range (IQR)969

Descriptive statistics

Standard deviation472.51801
Coefficient of variation (CV)1.1460417
Kurtosis-1.7294678
Mean412.30437
Median Absolute Deviation (MAD)63
Skewness0.50986801
Sum887279
Variance223273.27
MonotonicityNot monotonic
2024-08-14T16:28:22.503318image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83 303
 
11.6%
1020 211
 
8.1%
1004 143
 
5.5%
24 121
 
4.6%
3 115
 
4.4%
1000 99
 
3.8%
66 90
 
3.4%
51 80
 
3.1%
4 53
 
2.0%
26 53
 
2.0%
Other values (70) 884
33.8%
(Missing) 465
17.8%
ValueCountFrequency (%)
1 3
 
0.1%
3 115
4.4%
4 53
2.0%
11 4
 
0.2%
15 8
 
0.3%
16 26
 
1.0%
18 1
 
< 0.1%
19 23
 
0.9%
20 37
 
1.4%
23 11
 
0.4%
ValueCountFrequency (%)
1077 1
 
< 0.1%
1076 2
 
0.1%
1070 8
0.3%
1068 7
0.3%
1067 3
 
0.1%
1065 15
0.6%
1064 4
 
0.2%
1059 2
 
0.1%
1058 1
 
< 0.1%
1057 12
0.5%

total_seed_type
Categorical

MISSING 

Distinct3
Distinct (%)0.1%
Missing242
Missing (%)9.2%
Memory size20.6 KiB
net
1018 
actual
905 
gross
452 

Length

Max length6
Median length5
Mean length4.5237895
Min length3

Characters and Unicode

Total characters10744
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownet
2nd rownet
3rd rowgross
4th rowactual
5th rownet

Common Values

ValueCountFrequency (%)
net 1018
38.9%
actual 905
34.6%
gross 452
17.3%
(Missing) 242
 
9.2%

Length

2024-08-14T16:28:22.758788image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-14T16:28:22.941359image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
net 1018
42.9%
actual 905
38.1%
gross 452
19.0%

Most occurring characters

ValueCountFrequency (%)
t 1923
17.9%
a 1810
16.8%
n 1018
9.5%
e 1018
9.5%
c 905
8.4%
u 905
8.4%
l 905
8.4%
s 904
8.4%
g 452
 
4.2%
r 452
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10744
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 1923
17.9%
a 1810
16.8%
n 1018
9.5%
e 1018
9.5%
c 905
8.4%
u 905
8.4%
l 905
8.4%
s 904
8.4%
g 452
 
4.2%
r 452
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10744
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 1923
17.9%
a 1810
16.8%
n 1018
9.5%
e 1018
9.5%
c 905
8.4%
u 905
8.4%
l 905
8.4%
s 904
8.4%
g 452
 
4.2%
r 452
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10744
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 1923
17.9%
a 1810
16.8%
n 1018
9.5%
e 1018
9.5%
c 905
8.4%
u 905
8.4%
l 905
8.4%
s 904
8.4%
g 452
 
4.2%
r 452
 
4.2%

hatchery_name
Text

MISSING 

Distinct79
Distinct (%)3.7%
Missing465
Missing (%)17.8%
Memory size20.6 KiB
2024-08-14T16:28:23.342376image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length63
Median length49
Mean length23.35223
Min length2

Characters and Unicode

Total characters50254
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.5%

Sample

1st rowUD. Benur Ndaru Laut
2nd rowCV Raja Benur
3rd rowPT. Tri Karta Pratama - Total Kualitas Prima
4th rowPT. Central Proteinaprima Tbk
5th rowPT Windu Alam Sentosa
ValueCountFrequency (%)
pt 1115
 
13.2%
benur 480
 
5.7%
hatchery 414
 
4.9%
cv 402
 
4.8%
prima 376
 
4.5%
raja 303
 
3.6%
tani 260
 
3.1%
suri 259
 
3.1%
245
 
2.9%
pemuka 237
 
2.8%
Other values (142) 4343
51.5%
2024-08-14T16:28:24.156395image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6407
 
12.7%
6282
 
12.5%
r 3686
 
7.3%
i 2612
 
5.2%
P 2375
 
4.7%
e 2306
 
4.6%
t 2200
 
4.4%
n 2172
 
4.3%
T 2085
 
4.1%
u 1955
 
3.9%
Other values (58) 18174
36.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50254
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 6407
 
12.7%
6282
 
12.5%
r 3686
 
7.3%
i 2612
 
5.2%
P 2375
 
4.7%
e 2306
 
4.6%
t 2200
 
4.4%
n 2172
 
4.3%
T 2085
 
4.1%
u 1955
 
3.9%
Other values (58) 18174
36.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50254
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 6407
 
12.7%
6282
 
12.5%
r 3686
 
7.3%
i 2612
 
5.2%
P 2375
 
4.7%
e 2306
 
4.6%
t 2200
 
4.4%
n 2172
 
4.3%
T 2085
 
4.1%
u 1955
 
3.9%
Other values (58) 18174
36.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50254
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 6407
 
12.7%
6282
 
12.5%
r 3686
 
7.3%
i 2612
 
5.2%
P 2375
 
4.7%
e 2306
 
4.6%
t 2200
 
4.4%
n 2172
 
4.3%
T 2085
 
4.1%
u 1955
 
3.9%
Other values (58) 18174
36.2%
Distinct641
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Memory size20.6 KiB
2024-08-14T16:28:24.726468image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length27
Median length2
Mean length3.8085594
Min length1

Characters and Unicode

Total characters9967
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique307 ?
Unique (%)11.7%

Sample

1st rowH
2nd rowA1
3rd rowA3
4th rowB4
5th rowA
ValueCountFrequency (%)
a1 323
 
10.0%
1 157
 
4.9%
a2 151
 
4.7%
kolam 146
 
4.5%
a 113
 
3.5%
petak 79
 
2.5%
2 78
 
2.4%
a3 72
 
2.2%
b 51
 
1.6%
a4 49
 
1.5%
Other values (556) 1999
62.1%
2024-08-14T16:28:25.698821image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1121
 
11.2%
1 1044
 
10.5%
601
 
6.0%
2 554
 
5.6%
a 528
 
5.3%
B 457
 
4.6%
3 312
 
3.1%
0 288
 
2.9%
K 286
 
2.9%
o 232
 
2.3%
Other values (59) 4544
45.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9967
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1121
 
11.2%
1 1044
 
10.5%
601
 
6.0%
2 554
 
5.6%
a 528
 
5.3%
B 457
 
4.6%
3 312
 
3.1%
0 288
 
2.9%
K 286
 
2.9%
o 232
 
2.3%
Other values (59) 4544
45.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9967
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1121
 
11.2%
1 1044
 
10.5%
601
 
6.0%
2 554
 
5.6%
a 528
 
5.3%
B 457
 
4.6%
3 312
 
3.1%
0 288
 
2.9%
K 286
 
2.9%
o 232
 
2.3%
Other values (59) 4544
45.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9967
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1121
 
11.2%
1 1044
 
10.5%
601
 
6.0%
2 554
 
5.6%
a 528
 
5.3%
B 457
 
4.6%
3 312
 
3.1%
0 288
 
2.9%
K 286
 
2.9%
o 232
 
2.3%
Other values (59) 4544
45.6%

pond_length
Real number (ℝ)

HIGH CORRELATION 

Distinct339
Distinct (%)13.0%
Missing6
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean44.169694
Minimum3
Maximum650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2024-08-14T16:28:26.020988image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile12.65
Q130
median40
Q353
95-th percentile70.71
Maximum650
Range647
Interquartile range (IQR)23

Descriptive statistics

Standard deviation26.640087
Coefficient of variation (CV)0.60313045
Kurtosis178.94377
Mean44.169694
Median Absolute Deviation (MAD)10
Skewness9.34086
Sum115327.07
Variance709.69425
MonotonicityNot monotonic
2024-08-14T16:28:26.248096image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 287
 
11.0%
40 225
 
8.6%
30 171
 
6.5%
70.71 169
 
6.5%
32 92
 
3.5%
20 81
 
3.1%
60 65
 
2.5%
45 61
 
2.3%
25 54
 
2.1%
36.51 52
 
2.0%
Other values (329) 1354
51.7%
ValueCountFrequency (%)
3 13
 
0.5%
3.5 9
 
0.3%
4 2
 
0.1%
5 15
 
0.6%
6 3
 
0.1%
7 50
1.9%
8 4
 
0.2%
8.5 1
 
< 0.1%
9 1
 
< 0.1%
10 11
 
0.4%
ValueCountFrequency (%)
650 1
< 0.1%
577.35 1
< 0.1%
400 1
< 0.1%
222.5 2
0.1%
200 1
< 0.1%
196 2
0.1%
194 2
0.1%
192 2
0.1%
185 1
< 0.1%
140 1
< 0.1%

pond_width
Real number (ℝ)

HIGH CORRELATION 

Distinct384
Distinct (%)14.7%
Missing6
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean36.299874
Minimum1.5
Maximum650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2024-08-14T16:28:26.489808image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile10
Q126.23
median34.5
Q344.72
95-th percentile70.71
Maximum650
Range648.5
Interquartile range (IQR)18.49

Descriptive statistics

Standard deviation23.034006
Coefficient of variation (CV)0.63454784
Kurtosis288.35592
Mean36.299874
Median Absolute Deviation (MAD)8.8
Skewness12.49396
Sum94778.97
Variance530.56545
MonotonicityNot monotonic
2024-08-14T16:28:26.725401image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 195
 
7.5%
50 180
 
6.9%
40 171
 
6.5%
70.71 169
 
6.5%
32 102
 
3.9%
20 94
 
3.6%
25 84
 
3.2%
5 67
 
2.6%
27.39 49
 
1.9%
35 46
 
1.8%
Other values (374) 1454
55.6%
ValueCountFrequency (%)
1.5 1
 
< 0.1%
2 9
 
0.3%
3 13
 
0.5%
5 67
2.6%
6 5
 
0.2%
7 3
 
0.1%
7.75 1
 
< 0.1%
8 2
 
0.1%
8.03 5
 
0.2%
8.5 1
 
< 0.1%
ValueCountFrequency (%)
650 1
 
< 0.1%
500 1
 
< 0.1%
433.01 1
 
< 0.1%
160 1
 
< 0.1%
100 2
 
0.1%
86 1
 
< 0.1%
83.67 1
 
< 0.1%
80 1
 
< 0.1%
75 1
 
< 0.1%
70.71 169
6.5%

pond_depth
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct38
Distinct (%)1.5%
Missing118
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean9.4564346
Minimum0.6
Maximum13266
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2024-08-14T16:28:26.945840image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1
Q11
median1.3
Q31.5
95-th percentile2
Maximum13266
Range13265.4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation266.00059
Coefficient of variation (CV)28.129058
Kurtosis2472.3759
Mean9.4564346
Median Absolute Deviation (MAD)0.2
Skewness49.593065
Sum23631.63
Variance70756.315
MonotonicityNot monotonic
2024-08-14T16:28:27.171483image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1 605
23.1%
1.5 547
20.9%
1.2 454
17.3%
1.3 188
 
7.2%
2 178
 
6.8%
1.6 78
 
3.0%
1.1 69
 
2.6%
1.4 67
 
2.6%
1.8 42
 
1.6%
1.25 39
 
1.5%
Other values (28) 232
 
8.9%
(Missing) 118
 
4.5%
ValueCountFrequency (%)
0.6 3
 
0.1%
0.7 13
 
0.5%
0.8 27
 
1.0%
0.9 28
 
1.1%
1 605
23.1%
1.05 5
 
0.2%
1.1 69
 
2.6%
1.15 4
 
0.2%
1.2 454
17.3%
1.25 39
 
1.5%
ValueCountFrequency (%)
13266 1
 
< 0.1%
300 1
 
< 0.1%
160 2
 
0.1%
150 20
0.8%
140 1
 
< 0.1%
130 3
 
0.1%
120 18
0.7%
110 2
 
0.1%
75 5
 
0.2%
60 2
 
0.1%

Interactions

2024-08-14T16:28:07.416963image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:39.125914image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:41.789113image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:44.045375image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:46.953589image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:49.687917image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:52.401313image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:54.703170image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:57.293662image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:59.664621image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:02.757632image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:05.242881image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:07.606170image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:39.378173image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:41.955218image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:44.231919image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:47.203612image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:49.878263image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:52.576761image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:54.894566image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:57.473702image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:59.831109image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:02.931409image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:05.412653image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:07.786000image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:39.625201image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:42.125843image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:44.453140image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:47.438063image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:50.049783image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:52.752873image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:55.078649image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:57.639410image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:59.997158image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:03.088333image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:05.585971image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:07.979163image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:40.160371image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:42.316007image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:44.714221image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:47.706400image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:50.253479image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:52.945122image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:55.308152image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:57.834471image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:00.194548image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:03.296866image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:05.782483image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:08.165663image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:40.327018image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:42.486586image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:44.940018image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:47.947828image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:50.417181image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:53.107372image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:55.524251image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:58.011219image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:00.427429image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:03.506984image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:05.953560image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:08.357172image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:40.534353image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:42.673157image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:45.152160image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:48.215907image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:50.638321image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:53.309351image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:55.806154image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:58.204973image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:00.657846image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:03.743879image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:06.139650image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:08.526662image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:40.713661image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:42.889931image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:45.410863image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:48.466027image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:50.896478image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:53.482753image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:56.006294image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:58.376114image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:00.913294image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:04.003213image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:06.348335image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:08.759241image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:40.892106image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:43.155841image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:45.688620image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:48.712266image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:51.176491image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:53.666882image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:56.203077image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:58.566289image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:01.106324image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:04.265887image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:06.526796image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:09.017127image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:41.061477image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:43.325658image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:45.948897image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:48.884384image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:51.419443image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:53.844460image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:56.397803image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:58.734633image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:01.288429image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:04.519157image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:06.708788image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:09.280068image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:41.238734image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:43.494338image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:46.192661image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:49.054266image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:51.686787image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:54.012410image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:56.648934image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:58.973173image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:01.998550image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:04.703734image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:06.882193image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:09.586915image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:41.437859image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:43.676731image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:46.468258image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:49.247189image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:51.950077image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:54.252168image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:56.918821image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:59.236309image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:02.291369image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:04.886160image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:07.058180image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:09.839000image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:41.604064image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:43.871362image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:46.708464image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:49.505152image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:52.215833image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:54.467729image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:57.106989image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:27:59.403361image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:02.561816image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:05.058728image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-14T16:28:07.237809image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Correlations

2024-08-14T16:28:27.370706image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
areaextracted_athatchery_ididinitial_agelimit_weight_per_areapond_depthpond_idpond_lengthpond_widthspecies_idsubscription_typetarget_cultivation_daytarget_sizetotal_seedtotal_seed_type
area1.0000.0000.082-0.0270.057-0.1190.267-0.0570.8770.8810.0000.0000.1480.0540.5260.001
extracted_at0.0001.0000.0150.0250.1250.0000.0000.0000.1270.0000.0000.1340.0000.0000.0000.093
hatchery_id0.0820.0151.0000.2680.182-0.0950.0000.1550.1030.1040.4680.2130.1220.1050.1000.253
id-0.0270.0250.2681.000-0.0100.015-0.0840.796-0.0920.0210.2750.3100.0980.3360.1280.188
initial_age0.0570.1250.182-0.0101.000-0.0150.0740.0380.0280.0550.0000.184-0.0550.0730.1880.101
limit_weight_per_area-0.1190.000-0.0950.015-0.0151.0000.035-0.026-0.135-0.0820.0000.000-0.016-0.0570.1070.016
pond_depth0.2670.0000.000-0.0840.0740.0351.000-0.0740.2320.2831.0000.0000.130-0.0150.3400.000
pond_id-0.0570.0000.1550.7960.038-0.026-0.0741.000-0.1140.0050.5710.2210.0950.2380.0640.294
pond_length0.8770.1270.103-0.0920.028-0.1350.232-0.1141.0000.7880.6450.1320.1190.0560.4330.187
pond_width0.8810.0000.1040.0210.055-0.0820.2830.0050.7881.0000.8250.1740.1350.0540.5710.230
species_id0.0000.0000.4680.2750.0000.0001.0000.5710.6450.8251.0000.2350.6410.0200.3730.491
subscription_type0.0000.1340.2130.3100.1840.0000.0000.2210.1320.1740.2351.0000.1620.0000.1140.103
target_cultivation_day0.1480.0000.1220.098-0.055-0.0160.1300.0950.1190.1350.6410.1621.000-0.112-0.0380.177
target_size0.0540.0000.1050.3360.073-0.057-0.0150.2380.0560.0540.0200.000-0.1121.0000.0560.000
total_seed0.5260.0000.1000.1280.1880.1070.3400.0640.4330.5710.3730.114-0.0380.0561.0000.219
total_seed_type0.0010.0930.2530.1880.1010.0160.0000.2940.1870.2300.4910.1030.1770.0000.2191.000

Missing values

2024-08-14T16:28:10.296606image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-14T16:28:11.191260image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-14T16:28:11.872257image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idpond_idspecies_idtotal_seedstarted_atfinished_atremarkcreated_atupdated_atareainitial_agelimit_weight_per_areatarget_cultivation_daytarget_sizeextracted_atsubscription_typeordered_athatchery_idtotal_seed_typehatchery_namepond_namepond_lengthpond_widthpond_depth
018876362941.03319202022-10-14 00:00:00.0002023-01-29 00:00:00.000NaN2022-10-14 12:34:23.0002023-01-30 01:46:27.0004030.000.02.00110.035.02024-04-12 17:02:22.000FreeNaN66.0netUD. Benur Ndaru LautH65.0062.001.0
122118371021.0400002023-04-26 00:00:00.0002023-06-23 00:00:00.000#TBR012023-04-30 22:28:13.0002023-06-26 05:35:32.000399.000.01.25120.0100.02024-04-12 17:02:22.000FreeNaN83.0netCV Raja BenurA130.0020.001.0
22408839154NaN3575702023-08-01 00:00:00.0002023-10-18 00:00:00.000#SiklusFarm2023-07-12 01:13:05.0002023-10-19 04:23:11.0002000.000.01.25120.020.02024-04-12 17:02:22.000Free2023-07-12 01:13:05.0001004.0grossPT. Tri Karta Pratama - Total Kualitas PrimaA350.0040.001.4
317743209141.01682752022-07-19 00:00:00.0002022-09-29 00:00:00.000#SiklusCustomerPermadi2022-08-14 04:40:46.0002022-10-06 00:09:36.0001512.000.01.25100.055.02024-04-12 17:02:22.000FreeNaN3.0actualPT. Central Proteinaprima TbkB454.0028.001.5
417125341871.01880002022-07-07 00:00:00.0002022-09-20 00:00:00.000#SiklusTebar2022-07-12 00:24:30.0002022-10-15 04:27:24.0001225.000.01.25100.055.02024-04-12 17:02:22.000Free2022-07-12 00:24:30.00035.0netPT Windu Alam SentosaA35.0035.001.2
52807046261NaN2001282023-12-20 00:00:00.0002024-03-30 00:00:00.000NaN2023-12-12 04:54:37.0002024-04-06 00:06:16.000803.840.01.25120.055.02024-04-12 17:02:22.000Free2023-12-12 04:54:37.000NaNactualNaNB832.0032.001.5
626058441471.01480072023-10-02 00:00:00.0002024-03-03 00:00:00.000#SiklusFarm2023-10-03 03:12:22.0002024-03-08 03:46:12.0001254.000.01.25153.055.02024-04-12 17:02:22.000Free2024-02-07 00:00:00.00020.0netPT. Maju Tambak SumurK434.6434.651.3
726058441471.01480072023-10-02 00:00:00.0002024-03-03 00:00:00.000#SiklusFarm2023-10-03 03:12:22.0002024-03-08 03:46:12.0001254.000.01.25153.055.02024-04-12 17:02:22.000Free2024-02-03 10:36:04.00020.0netPT. Maju Tambak SumurK434.6434.651.3
82470234746NaN4396352023-07-19 00:00:00.0002023-07-29 00:00:00.000NaN2023-08-08 10:55:46.0002023-09-08 10:43:35.00035.000.01.2510.055.02024-04-12 17:02:22.000FreeNaN1020.0actualMATERNIDADE NACALAN137.005.001.0
924007347462.05010542023-07-08 00:00:00.0002023-07-17 00:00:00.000NaN2023-07-10 07:08:53.0002023-08-07 09:57:49.00035.000.01.259.055.02024-04-12 17:02:22.000FreeNaN1020.0actualMATERNIDADE NACALAN137.005.001.0
idpond_idspecies_idtotal_seedstarted_atfinished_atremarkcreated_atupdated_atareainitial_agelimit_weight_per_areatarget_cultivation_daytarget_sizeextracted_atsubscription_typeordered_athatchery_idtotal_seed_typehatchery_namepond_namepond_lengthpond_widthpond_depth
26071744934490NaN3800002022-07-02 00:00:00.0002022-10-07 00:00:00.000NaN2022-07-28 03:33:46.0002023-09-22 03:26:47.0001056.00.01.25120.055.02024-04-12 17:02:22.000FreeNaN1000.0grossAGAPE HatcheryDungkek133.0032.001.8
26081961433945NaN4000002022-12-01 00:00:00.0002023-02-17 00:00:00.000NaN2022-11-20 07:29:20.0002023-02-21 10:04:55.000699.90.04.00100.045.02024-04-12 17:02:22.000FreeNaN24.0netPT. Prima LarvaePond #130.0030.001.5
260928726456961.01224002024-01-05 00:00:00.0002024-03-26 00:00:00.000#TBRBanyuwangi20242024-01-08 01:33:32.0002024-04-02 01:37:29.000500.00.01.25100.030.02024-04-12 17:02:22.000FreeNaN83.0grossCV Raja BenurA125.0020.001.5
261025362435781.02210002023-10-16 00:00:00.0002024-01-31 00:00:00.000NaN2023-09-07 04:00:41.0002024-02-07 00:06:25.000314.00.04.77110.050.02024-04-12 17:02:22.000Free2023-09-07 04:52:05.000NaNgrossNaNKolbun 320.0020.001.2
261124771428261.02262842023-08-10 00:00:00.0002023-11-13 00:00:00.000#SiklusFarm2023-08-10 03:03:54.0002023-12-11 08:38:31.0001400.00.01.25120.055.02024-04-12 17:02:22.000Free2023-08-10 03:03:54.00020.0actualPT. Maju Tambak SumurA140.0035.001.2
261219131347122.03000002022-10-25 00:00:00.0002023-01-21 00:00:00.000NaN2022-10-25 10:13:31.0002023-01-25 08:01:19.0004999.90.01.2588.055.02024-04-12 17:02:22.000FreeNaN1020.0actualMATERNIDADE NACALAE0170.7170.711.5
261327552456971.01000002023-11-05 00:00:00.0002024-02-13 00:00:00.000#TBR032023-11-18 03:43:40.0002024-03-27 02:52:36.000900.00.01.25120.055.02024-04-12 17:02:22.000Free2023-11-18 03:43:40.0001022.0netWindu Segara AnyarKolam 445.0020.001.5
261427885245701.0307162023-12-02 00:00:00.0002024-02-29 00:00:00.000#TBR052023-12-03 03:06:01.0002024-03-03 03:03:26.00086.00.01.25120.020.02024-04-12 17:02:22.000FreeNaN83.0grossCV Raja BenurBagus10.718.031.0
261515868323981.01500002022-04-28 00:00:00.0002022-06-21 00:00:00.000#SiklusTebar2022-05-08 16:02:51.0002022-08-23 03:12:20.0001600.00.01.25100.055.02024-04-12 17:02:22.000Free2022-05-08 16:02:51.00035.0grossPT Windu Alam SentosaB 4.145.0035.002.0
261620849359131.03840002023-02-09 00:00:00.0002023-04-07 00:00:00.000NaN2023-02-14 00:23:40.0002023-04-14 00:04:22.0002915.00.01.25100.040.02024-04-12 17:02:22.000FreeNaN83.0actualCV Raja BenurA455.0053.001.7

Duplicate rows

Most frequently occurring

idpond_idspecies_idtotal_seedstarted_atfinished_atremarkcreated_atupdated_atareainitial_agelimit_weight_per_areatarget_cultivation_daytarget_sizeextracted_atsubscription_typeordered_athatchery_idtotal_seed_typehatchery_namepond_namepond_lengthpond_widthpond_depth# duplicates
222780406011.01605002023-05-28 00:00:00.0002023-08-15 00:00:00.000NaN2023-05-30 13:11:09.0002023-08-15 10:55:45.000999.00.01.25120.035.02023-09-05 17:02:11.000FreeNaNNaNgrossNaNA137.0027.000.93
0954522914NaN2574002021-05-08 00:00:00.0002021-07-15 00:00:00.000NaN2021-05-05 06:14:42.0002022-06-08 06:20:58.0001526.00.01.25120.045.02024-04-12 17:02:22.000Free2021-05-05 06:25:18.0004.0netPT. Summa BenurA144.7233.541.42
115060310331.047692022-03-21 00:00:00.0002022-07-04 00:00:00.000NaN2022-03-21 13:58:04.0002022-07-12 00:08:41.0007.0NaN1.25120.045.02024-04-12 17:02:22.000FreeNaN66.0actualUD. Benur Ndaru LautKolam 123.502.000.92
323326347172.02750002023-07-18 00:00:00.0002023-12-22 00:00:00.000NaN2023-06-17 07:39:44.0002023-12-27 17:52:49.0004999.90.01.25157.055.02024-04-12 17:02:22.000FreeNaN1020.0actualMATERNIDADE NACALAF0170.7170.711.52
423327347192.02750002023-07-19 00:00:00.0002024-01-04 00:00:00.000NaN2023-06-17 07:39:45.0002024-01-06 09:31:41.0004999.90.01.25169.055.02024-04-12 17:02:22.000FreeNaN1020.0actualMATERNIDADE NACALAF0370.7170.711.52
523328347202.02750002023-07-14 00:00:00.0002024-01-03 00:00:00.000NaN2023-06-17 07:39:45.0002024-01-06 10:53:02.0004999.90.01.25173.055.02024-04-12 17:02:22.000FreeNaNNaNactualNaNF0470.7170.711.52
623329347122.02750002023-07-17 00:00:00.0002024-01-02 00:00:00.000NaN2023-06-17 07:39:46.0002024-01-04 09:53:15.0004999.90.01.25169.055.02024-04-12 17:02:22.000FreeNaN1020.0actualMATERNIDADE NACALAE0170.7170.711.52
723330347132.02750002023-07-17 00:00:00.0002023-12-28 00:00:00.000NaN2023-06-17 07:39:46.0002023-12-29 14:12:17.0004999.90.01.25164.055.02024-04-12 17:02:22.000FreeNaN1020.0actualMATERNIDADE NACALAE0270.7170.711.52
823331347142.02750002023-07-14 00:00:00.0002023-12-26 00:00:00.000NaN2023-06-17 07:39:47.0002023-12-29 14:15:29.0004999.90.01.25165.055.02024-04-12 17:02:22.000FreeNaN1020.0actualMATERNIDADE NACALAE0370.7170.711.52
92337141507NaN4900002023-05-16 00:00:00.0002023-08-14 00:00:00.000NaN2023-06-18 08:17:26.0002023-08-15 06:58:11.0002106.00.01.25100.045.02024-04-12 17:02:22.000FreeNaN23.0actualPT. Suryawindu Pertiwi - Shrimp HatcheryA254.0039.001.22